Cellular responses to hormones and neurotransmitters can be either binary or graded, and they are usually transient. The G protein-mediated pheromone response in yeast is typical. Our central hypothesis is that mathematical techniques developed for studying stochastic processes and dynamical systems can explain the complex behaviors of cell signaling networks. The objective of this proposal is to understand the role of feedback control mechanisms used in the pheromone response pathway of yeast. Specifically, we will determine which feedback loops are responsible for maintaining the graded response, which interactions control the time-dependent dynamical response, and which interactions are important for desensitization. Our approach will be to activate the yeast pheromone signal at different points throughout the pathway, conduct a quantitative analysis of the transcription response, devise computational models that describe the observed activity, and test the validity of each model through experimentation. We will consider both deterministic models for the average protein concentration levels and stochastic models that take into account the random nature of biochemical reactions. There are three specific aims to this proposal: Aim 1: What is the basis for the conversion from a graded to binary response following G protein activation? We will consider three hypothetical mechanisms capable of producing a binary response: stochastic switching, bistability, and critical slowing down. We will distinguish between these models by analyzing G protein activation in individual cells and cell populations. Aim 2: Which pathway components downstream of the G protein moderate a graded to binary response? We will activate the pathway at points downstream of the G protein and identify additional downstream mechanisms for converting the graded response to a binary one. Aim 3: What is the biological function of the various feedback controls in the pheromone response pathway? Pheromone-induced or repressed proteins are likely to represent feedback regulators of the pathway. We will identify these proteins and test the consequence of altering their expression on pathway responsiveness. An integrated computational and experimental analysis of the pheromone regulation in yeast will eventually lead to improved predictive models of signaling events in more complex organisms including humans.